Abstract
Rule extraction from artificial neural networks (ANN) provides a mechanism to interpret the knowledge embedded in the numerical weights. Classification problems with continuous-valued parameters create difficulties in determining boundary conditions for these parameters. This paper presents an approach to locate such boundaries using sensitivity analysis. Inclusion of this decision boundary detection approach in a rule extraction algorithm resulted in significant improvements in rule accuracies.
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[Baum 1991] EB Baum, Neural Net Algorithms that Learn in Polynomial Time from examples and Queries, IEEE Transactions on Neural Networks, 2(1), 1991, pp 5–19.
[Cohn et al 1994] D Cohn, L Atlas, R Ladner, Improving Generalization with Active Learning, Machine Learning, Vol 15, 1994, pp 201–221.
[Craven et al 1993] MW Craven and JW Shavlik, 1993. Learning Symbolic Rules using Artificial Neural Networks, Proceedings of the Tenth International Conference on Machine Learning, Amherst: USA, pp. 79–95.
[Engelbrecht et al 1996] AP Engelbrecht, I Cloete, A Sensitivity Analysis algorithm for Pruning Feedforward Neural Networks, IEEE International Confernece in Neural Networks, Washington, Vol 2, 1996, pp 1274–1277
[Engelbrecht et al 1998a] AP Engelbrecht and I Cloete, 1998. Selective Learning using Sensitivity Analysis, 1998 International Joint Conference on Neural Networks (IJCNN'98), Alaska: USA, pp. 1150–1155.
[Engelbrecht 1998b] AP Engelbrecht, 1998. Sensitivity Analysis of Multilayer Neural Networks, submitted PhD dissertation, Department of Computer Science, University of Stellenbosch, Stellenbosch: South Africa.
[Fu 1994] LM Fu, Rule Generation from Neural Networks, IEEE Transactions on Systems, Man and Cybernetics, Vol 24, No 8, August 1994, pp 1114–1124.
[Hwang et al 1991] J-N Hwang, JJ Choi, S Oh, RJ Marks II, Query-Based Learning Applied to Partially Trained Multilayer Perceptrons, IEEE Transactions on Neural Networks, 2(1), January 1991, pp 131–136.
[Sestito et al 1994] S Sestito and TS Dillon, 1994. Automated Knowledge Acquisition, Prentice-Hall, Sydney: Australia.
[Towell 1994] GG Towell and JW Shavlik, Refining Symbolic Knowledge using Neural Networks, Machine Learning, Vol. 12, 1994, pp 321–331.
[Viktor et al 1995] HL Viktor, AP Engelbrecht and I Cloete, 1995. Reduction of Symbolic Rules from Artificial Neural Networks using Sensitivity Analysis, IEEE International Conference on Neural Networks (ICNN'95), Perth: Australia, pp. 1788–1793.
[Viktor et al 1998a] HL Viktor, AP Engelbrecht, I Cloete, Incorporating Rule Extraction from ANNs into a Cooperative Learning Environment, Neural Networks & their Applications (NEURAP'98), Marseilles, France, March 1998, pp 386–391.
[Viktor 1998] HL Viktor, 1998. Learning by Cooperation: An Approach to Rule Induction and Knowledge Fusion, submitted PhD dissertation, Department of Computer Science, University of Stellenbosch, Stellenbosch: South Africa.
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© 1999 Springer-Verlag Berlin Heidelberg
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Engelbrecht, A., Viktor, H. (1999). Rule improvement through decision boundary detection using sensitivity analysis. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100474
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DOI: https://doi.org/10.1007/BFb0100474
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